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Serie Economía
Nº 307 CAPITAL CONTROLS AND THE COST OF DEBT
EUGENIA ANDREASEN, MARTIN SCHINDLER Y PATRICIO VALENZUELA
Capital Controls and the Cost of Debt
January 20, 2015
Eugenia Andreasen, Martin Schindler, Patricio Valenzuela
ABSTRACT
Using a novel panel data set for international corporate bonds and capital account
restrictions in advanced and emerging economies, we find that restrictions on capital
inflows produce a substantial and economically meaningful increase in corporate
bond spreads. By contrast, we find no robust significant effect of restrictions on
outflows. The effect of capital account restrictions on inflows is particularly strong
for bonds maturing in the short-term, issued by small firms and in countries with
underdeveloped financial markets. Additionally, the paper shows that capital account
restrictions on inflows have a greater effect during periods of financial distress than
during periods of financial stability. These results are suggestive of a causal
interpretation of the estimated effects and establish a novel channel through which
capital controls affect economic outcomes.
JEL CODE: F3, F4, G1, G3
KEY WORDS: Credit spreads; Capital account restrictions; Financial instability; Financial openness
Andreasen
is at the University of Santiago of Chile, Schindler is at the Joint Vienna Institute, and Valenzuela is
at the University of Chile. Emails: [email protected] (E. Andreasen); [email protected] (M.
Schindler); [email protected] (P. Valenzuela). We have benefited from helpful comments from Arpad
Abraham, Franklin Allen, Elena Carletti, Giancarlo Corsetti, Oren Levintal, Jun “QJ” Qian, and seminar
participants at the Central Bank of Chile, the University of Santiago de Chile, and the 2014 Latin America and
the Caribbean Economic Association Annual Meeting. Patricio Valenzuela wishes to thank Fondecyt Initiation
Project # 11130390.
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1. Introduction
Over the past four decades, the global economy has become ever more financially
integrated, engendering not only a range of potential benefits, such as a more efficient
allocation of capital and better risk diversification, but also greater vulnerability to shocks, as
adverse shocks can more easily travel from one economy to another. Reflecting concerns
about the risks of financial globalization, the belief that countries that routinely block capital
flows, such as China and India, were insulated from financial turmoil during 2007-09 has
spurred renewed interest in understanding the effects of capital controls (Ostry et al., 2010).
In a re-assessment of the advantages and disadvantages of capital controls, researchers at the
International Monetary Fund (IMF) now consider capital controls to be a useful part of the
toolkit when countries have few other options (Blanchard and Ostry, 2012), while others have
gone further in suggesting that capital controls could become a regular policy tool (Jeanne,
Subramanian and Williamson, 2012). The change in sentiment is also reflected in the increased
use of capital controls in recent years, indicating the possibility of a reversal of the previous
trend toward freer capital markets.
Despite the potential benefits of capital controls from a prudential macroeconomic
perspective, any such benefits should be weighed against the cost of constraining the financial
opportunities of firms. Extant empirical research on capital account restrictions mainly focuses
on the impact of stock market liberalizations and on their effect on the firms’ cost of equity
capital (see, e.g., Bae, Bailey, Mao, 2006; Henry, 2000a, 2000b, 2003; Mitton, 2006; Gupta and
Yuan, 2009; Alfaro et al., 2014). However, recent studies show that debt issues in public
markets are a more important source of capital than equity issues for firms and that debt
markets are more internationalized than equity markets (Gozzi et al., 2010). Nevertheless, very
little is known about the effects of capital controls on the cost of debt capital. Therefore,
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improving our understanding of this link, as well as whether the effects of capital controls on
the cost of debt capital are symmetric for restrictions on inflows and outflows, is an important
area in which more research is needed.
In addition, the impact of capital account restrictions on firms’ ability to finance their
operations by issuing debt is likely shaped by a number of factors, including the time to
maturity, the size of firms, the development and depth of domestic financial markets, and the
broader economic environment. Thus, a number of important questions arise: Are bonds
maturing in the short run more affected by capital controls? Do larger firms tend to have a
greater capacity than small firms for mitigating the impact of regulatory restrictions? Are
capital controls less binding in more developed financial markets in which domestic firms are
less dependent on foreign lending than in less developed financial markets? Is the impact of
controls magnified during times of financial distress?
This paper addresses all of these issues by using a new data set for corporate bonds placed
in international markets by advanced and emerging market borrowers. The key finding is that
restrictions on capital inflows produce a substantial and economically meaningful increase in
corporate bond spreads. By contrast, there is no robust significant effect of outflow
restrictions. The effect of capital account restrictions on inflows is particularly strong for
bonds maturing in the short-term, issued by small firms, and issued in countries with
underdeveloped financial markets. Additionally, the paper demonstrates that capital account
restrictions on inflows have a greater effect during periods of financial distress than during
periods of financial stability.
These findings are consistent with a causal interpretation in which restrictions on capital
inflows worsen the conditions for firms’ access to capital in international markets, rendering
them more vulnerable to shocks, particularly when financial markets are under distress. They
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are also consistent with the finding that capital controls have been asymmetric in recent
decades, as they have been designed to discourage capital inflows (Reinhart and Smith, 2002).
The main results of this paper are statistically significant even after we control for the standard
determinants of corporate bond spreads, for the potential effects of other structural reforms,
and for capital restrictions on outflows. Moreover, these results are robust to the inclusion of
firm and time fixed effects.
The remainder of the paper is organized as follows. Section 2 provides a brief overview of
the related literature. Section 3 describes the data and summary statistics. Section 4 presents
our econometric framework and our main results. Section 5 explores some specific channels
through which capital account restrictions affect corporate bond spreads. Section 6 presents a
set of robustness checks. Section 7 concludes.
2. Related literature
Although a large body of research exists on the effects of capital account restrictions,
whether it is optimal for countries to liberalize their capital accounts remains an open empirical
question. While theory predicts that financial openness provides a number of benefits,
empirical results are not conclusive. A number of studies suggest that reducing capital account
restrictions leads to higher growth, productivity, investment, and equity prices (Bekaert et al.,
2005, 2011; Gupta and Yuan, 2009; Henry, 2000a, 2000b; Quinn and Toyoda, 2008); lower
consumption growth volatility (Bekaert et al, 2006); increased domestic financial access
(Fischer and Valenzuela, 2013); reduced financial constraints (Love, 2003; Laeven, 2003;
Harrison, Love and McMillan, 2004; Forbes, 2007); and better quality corporate credit ratings
(Prati, Schindler and Valenzuela, 2012). Other studies, by contrast, argue that capital account
restrictions may render economies less vulnerable to crises owing to their potential effect on
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firms’ capital debt structure (De Gregorio et al., 2000; Gallego and Hernandez, 2003) and
reduce aggregate wage inequality (Larrain, 2014).
As emphasized by Prati, Schindler, and Valenzuela (2012), three factors are likely to largely
account for the lack of conclusive empirical results on capital account restrictions: the use of
aggregated data, the potential presence of endogeneity bias, and the lack of sufficiently refined
measures of financial openness. Aggregate data may hide important heterogeneities to the
extent that different subsets of an economy are affected, making it difficult to detect significant
average effects. In addition, most widely used capital control indicators are crude measures
that ignore variations in the degree of capital account restrictiveness, further curtailing the
possibility of elucidating the potential costs and benefits of financial openness. Finally, the
current and future performance of a country may influence the decision of policy makers to
financially integrate with the rest of the world. Therefore, estimations that do not control for
potential endogeneity may produce biased results.
This paper addresses the shortcomings of the literature in at least three ways. First, the
broad, bond-level panel data set that is used in this paper allows us to explore a variety of
heterogeneities that are suggestive of a causal interpretation of the estimated effects. We
explore four primary dimensions through which capital controls could have a differentiated
effect on corporate credit spreads: time to maturity, firm size, domestic financial development,
and global financial distress. If the relationship between capital controls and corporate credit
risk reflects merely a simple correlation caused by common variables such as macroeconomic
or global factors rather than a causal effect, heterogeneous effects would not be found.
However, we find that all these effects are significant and economically relevant.
The strategy of exploiting potential heterogeneous effects is also used in recent research
that examines the effects of stock market liberalization but that lacks suitable instruments to
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address endogeneity directly. For instance, Mitton (2006) reports that the effect of stock
market liberalization on firm performance is significantly stronger among firms that pay out
lower dividends. Forbes (2007) shows that smaller firms experienced more significant financial
constraints than larger firms during the 1991-1998 Chilean encaje. Alfaro, Chari, and Kanczuk
(2014) find that the stock returns of large firms and the largest exporting firms were less
affected by the imposition of capital controls in Brazil during 2008-2009.
Second, the use of microeconomic data also allows us to attenuate potential endogeneity
problems. Given that capital account liberalizations during times of financial market distress
may generate complaints about selling off the country at fire sale prices, policymakers tend to
be more inclined to eliminate capital controls during times of financial stability (Summers,
1994). Therefore, results from studies that utilize country-level measures of firm performance,
such as stock price indexes (see, e.g., Henry, 2000b), are more likely to be driven by reverse
causality bias than studies that use micro-level data. In addition, using bond-level data allows
us to attenuate problems associated with omitted variables in three ways: a) with firm fixed
effects that control for endogeneity arising from time-invariant firm characteristics, b) with
time fixed effects that control for endogeneity arising from variation in global factors, such as
the world business cycle or the fad effect of capital account liberalizations, and c) with bondand firm-level variables that control for bond and firm characteristics that change over time.
Third, this paper uses detailed measures of capital account restrictions that capture more
subtle differences in capital control regimes across countries and time. Moreover, our data on
capital controls can be disaggregated in novel ways (e.g., by direction of flows or type of
transactions), allowing for additional and innovative tests of our hypotheses. Such innovative
tests are important, as identifying a significant link between capital controls and economic
outcomes is complicated by the fact that some of the most widely used capital controls
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indicators are crude, binary indicators that ignore variations in the degree of capital account
restrictiveness.
A number of studies indirectly assess the impact of removing capital controls on the cost
of capital by measuring how capital controls affect firm financing constraints and stock prices
(Love, 2003; Laeven, 2003; Harrison, Love and McMillan, 2004; Forbes, 2007; Alfaro, Chari,
and Kanczuk, 2014). Nonetheless, to our knowledge, this is the first empirical paper to directly
study how capital account restrictions affect the cost of debt financing, as measured by
corporate bond spreads, and whether this effect is asymmetric across different types of capital
account restrictions.
3. Data
For the purpose of this paper, we merge two novel data sets. The first data set contains
information on corporate bonds placed in international markets by developed and emerging
market borrowers. The second data set contains information on capital account restrictions.
The merged data covers the sample period from 2005:Q1 to 2009:Q2.
The data set for international corporate bonds builds on the one used in Valenzuela (2013).
It considers all fixed-rate bonds denominated in U.S. dollars available in Bloomberg as of June
2009, with the exception of bonds issued by firms located in the U.S. or England.1 The majority
of bonds that are included in our sample correspond to Yankee bonds, Euro-Dollar bonds,
and Global bonds.
These two countries have fully liberalized capital accounts throughout the sample period. Thus, their exclusion
matters little for our study, which focuses on the effects of changes in capital account regulations on corporate
bond spreads.
1
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We construct our data set for capital account restrictions by using the methodology
introduced by Schindler (2009), which is based on information provided in the IMF’s Annual
Report on Exchange Arrangements and Exchange Restrictions (AREAER).
To reduce the potential for errors in the coding of the data, we clean the data set in four
ways. First, we eliminate the top and bottom 0.5% of the spreads from our analysis. Second,
we drop all observations in the accounting variables that exceed the sample mean by more
than five standard deviations. Third, we do not consider bonds issued in countries where the
total number of observations is fewer than 30. Fourth, we restrict the sample to bonds that
are issued by firms with a Standard and Poor (S&P) credit rating between AAA and B-. After
cleaning the data, we obtain a final sample including all of our control variables that contains
3,740 bond-quarter observations.
Note that the sample that is used in this paper contains only firms that issue international
bonds denominated in U.S. dollars. Given that only certain types of firms choose, and are able
to access offshore financing, the results in this paper cannot be extrapolated to the entire
universe of firms. However, research on international debt financing denominated in U.S.
dollars is important. Gozzi et al. (2010) indicate that debt issues in public markets are a more
important source of capital for firms than equity issues and that debt markets are more
internationalized than equity markets. In emerging market economies, approximately 28% of
the total amount raised through equity issues is raised abroad, while debt issues abroad
represent 47% of the total amount raised through debt issues. In developed economies, equity
and debt issues abroad represent 8% and 35%, respectively, of the total amount raised.
Moreover, international debt issues tend to be denominated in foreign currencies, particularly
U.S. dollars (Hausmann and Panizza, 2011; Gozzi et al., 2012). As demonstrated in Valenzuela
(2013), the data on corporate bond spreads that are used in the paper are representative of the
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universe of bonds denominated in U.S. dollars. Therefore, the presented results are unlikely
to be driven by sample selection bias.2
3.1. Corporate bond spreads
The dependent variable is the corporate option-adjusted spread (OAS) from Bloomberg
Professional. The OAS measures the yield on a corporate bond in excess of a comparable U.S.
Treasury security, after accounting for the value of any embedded option (Fabozzi, 2006). 3
The use of the OAS in this study is important, as many corporate bonds contain embedded
options. Indeed, approximately 60% of the bonds in our sample contain contingent cash flows
owing to call, put or sink features. Notably, the OAS methodology does not affect the main
results in this paper, as they are robust to the use of a subsample of bonds without embedded
options. The OAS of a bond without any embedded option (i.e., a non-callable bond) is
computed as the constant spread that must be added to the spot interest rate to make the price
of the risk-free bond identical to the observed market price of the corporate bond.
3.2. Capital account restrictions
In line with Schindler (2009), this paper uses two measures of capital account restrictions
that allow us to identify the channels through which capital account restrictions affect
corporate bond spreads. The first measure captures capital account restrictions on inflows
(KA_IN). This measure is the simple average of eight dummy variables that capture
Valenzuela (2013) compares the average OASs from his data with OAS indexes reported by Bank of America
(BofA) Merrill Lynch for identical credit rating categories. Although some discrepancies exist between the series,
the indexes constructed from the data set used in this paper adequately mimic the behavior of the BofA Merrill
Lynch OAS indexes.
3 For details on the OAS computation see Cavallo and Valenzuela (2010). Other studies using OASs include, for
example, Becchetti et al. (2010), Huang and Kong (2003), and Pedrosa and Roll (1998).
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restrictions on capital account transactions that involve (1) the sale or issue of financial assets
abroad by residents and (2) the purchase of financial assets locally by nonresidents.
The second measure represents capital account restrictions on outflows (KA_OUT). This
measure is the simple average of eight dummy variables that capture restrictions on capital
account transactions that involve (1) the sale or issue of financial assets locally by nonresidents
and (2) the purchase of financial assets abroad by residents. Table I reports the transaction
categories that are used in this study and that are subject to capital account restrictions
according to the AREAER.
3.3. Other corporate bond spread determinants
To control for all variables that could directly affect corporate bond spreads, in all
specifications, we consider the standard determinants of corporate bond spreads according to
structural credit risk models and the empirical literature on the determinants of corporate bond
spreads (Merton, 1974; Collin-Dufresne et al., 2001; Campbell and Taksler, 2003). We also
consider a comprehensive set of country-level variables to control for the potential effects of
other reforms that may affect a country’s growth prospects.
At the bond level, our baseline regressions control for years to maturity, issue size, and
coupon rate. At the firm level, control variables include the S&P corporate credit rating, to
reflect the long-term and structural components of default risk (Löffler, 2004), as well as the
issuer’s equity volatility and a standard set of accounting variables (Campbell and Taskler,
2003). Firm-level performance indicators in our empirical model include firm size and the
ratios of operating income to sales, short-term debt to total debt, and total debt to assets.
Since financial, macroeconomic, and political reforms are usually part of an entire package
of structural reforms, to ensure that our results do not capture the effects of other
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contemporaneous reforms, we include a set of country-level variables in all our regressions. In
line with Bekaert, Campbell and Lundblad (2011), we consider private credit to GDP, private
bond market capitalization to GDP, public bond market capitalization to GDP, trade to GDP,
and political risk.4 Additionally, we also consider the growth rate of the economy and the GDP
per capita to control for growth opportunities and economic development. Finally, because
sovereign credit ratings are a significant determinant of corporate credit risk (Borensztein et
al., 2013), we also include them as part of our control variables.
Table II presents the definitions, units, and sources of the variables that are used in this
paper, and Table III reports the descriptive statistics of the variables.
4. Empirical analysis and main results
The central aim of this study is to explore whether capital account restrictions affect
corporate bond spreads while distinguishing between the effect of capital account restrictions
on inflows (KA_IN) and the effect of capital account restrictions on outflows (KA_OUT).
Our baseline econometric model is thus as follows:
Bond Spreadbfct= α + βXbfct+ φYfct+ δZct + γKA_INct + θKA_OUTct + Af + Bt + εbfct,
where the subscript ‘bfct’ refers to bond b, firm f, country c, and time t. Af is a vector of either
industry or firm dummy variables that account for industry or firm fixed effects, depending
on the regression. Industry and firm fixed effects control for endogeneity arising from timeinvariant industry and firm characteristics, respectively. Bt is a vector of time dummy variables
4
The political risk measure is a survey-based assessment of political stability contained in the ICRG database.
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accounting for time fixed effects that control for variations in global factors such as the world
business cycle or the fad effect of capital account liberalization. Xbfct is a set of bond
characteristics, Yfct is a set of firm-level performance indicators, Zct is a set of macroeconomic
variables, and εbfct is the error term. Our main parameters of interest are γ and θ.
Given that the sample used in this paper includes bond issued by firms located in countries
easing or tightening capital account restrictions at different moments of time, our specification
including firm and time fixed effects is analogous to a difference-in-differences estimator in a
setting with multiple-treatment-groups and multiple-time-periods (Imbens and Wooldridge,
2009). The identification assumption is that the control firms (bonds), independently of
whether they are located in countries that have already easing or tightening capital account
restrictions or have not, are exposed to similar global shocks as the treated firms (bonds)
around changes in the degree of financial openness. We believe this is a plausible assumption
given the homogeneous nature of the bonds included in the sample (i.e., international bonds
denominated in U.S. dollars).
Table IV presents the results from the estimation of the baseline regression by ordinary
least squares (OLS) with errors clustered by bond. Columns 1 and 2 report the results for our
baseline specification with industry and firm fixed effects, respectively. The results suggest that
capital account restrictions on inflows and outflows have sharply asymmetric effects: capital
account restrictions on inflows increase corporate bond spreads with a statistically significant
and economically meaningful magnitude. That is, one-standard-deviation increase in KA_IN
increases corporate bond spreads by between 37 and 55 basis points. By contrast, capital
account restrictions on outflows tend to decrease corporate bond spreads; however, this result
is not robust to the inclusion of firm fixed effects.
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The result regarding the effect of capital account restrictions on inflows seems intuitive
for two reasons. First, firms residing in a country with restrictions on capital inflows have
fewer opportunities for raising foreign capital. Second, when capital account restrictions on
inflows are in place, firms incur additional costs when raising capital. These higher costs can
arise from a variety of channels. They can result from the higher taxes or fees on capital flows
related to the capital account restriction. Additionally, financial resources can become more
expensive as a result of the restricted supply. On top of that, the reduced market liquidity
resulting from the restricted capital flows might also increase the liquidity premium in debt
markets.5
Fewer or more expensive sources of capital make firms notably more vulnerable by
reducing their ability to diversify, to exploit profitable investment opportunities, and to
rollover existing debt denominated in foreign currency. Moreover, in the context of structural
credit risk models, higher financing costs reduce firm equity value and increase default
probabilities and credit spreads.
The asymmetric effect of different types of capital controls highlights the importance of
distinguishing between the effect of capital account restrictions on inflows and the effect of
capital account restrictions on outflows, as they are policy tools with different purposes.
Usually, capital controls on inflows have been used as a crisis prevention tool, while capital
controls on outflows have a long tradition of use as a crisis containment tool (Demirguc-Kunt
and Serven, 2010). Therefore, studies using aggregate indexes of capital account restrictions
Studies on equity markets suggest that stock markets tend to become more liquidity following capital control
liberalizations (Levine and Zervos, 1998) and that increased stock market liquidity reduce the equity premium
(Ahimud and Mendelson, 1986; Amihud et al, 1997). There is also a rich literature that shows that debt market
illiquidity is a significant determinant of corporate bond spreads (Chen, Lesmond, and Wei, 2007; Covitz and
Downing, 2007; Bao, Pan and Wang, 2010; Valenzuela, 2013).
5
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may hide important asymmetries to the extent that different types of controls have different
effects in financial markets, making it difficult to detect significant average effects.
Most of the coefficients for our control variables have the expected signs, and many of
them are significantly associated with corporate credit spreads. Consistent with the predictions
of structural credit risk models, the results from our specification including firm-fixed effects
show that equity volatility is positively related to credit spreads (Merton, 1974; Campbell and
Taskler, 2003). As expected, firms with higher quality credit ratings exhibit smaller credit
spreads, and the short-term debt over total debt ratio is positive and highly significant in the
regression. This last result is consistent with the argument that a higher proportion of shortterm debt exposes firms to rollover risk (Gopalan, Song and Yerramilli, 2013; Valenzuela,
2013). At the macro level, the results indicate that trade over GDP, sovereign credit ratings,
and economic growth are negatively related to credit spreads. On the other hand, a higher
ratio of public bond market capitalization to GDP is associated with higher credit spreads.
This finding is consistent with findings indicating that countries with excessive debt are more
prone to costly financial crises (Arcand, Berkes and Panizza, 2012; Law and Singh, 2014) and
that high levels of sovereign debt are likely to affect corporate bond spreads through sovereign
risk (Borensztein, Cowan and Valenzuela, 2013).
5. Narrowing down the channels
This section explores whether potential heterogeneities exist in the impact of capital
account restrictions on corporate bond spreads and whether they are consistent with a causal
relationship between capital controls and the cost of debt. Specifically, we examine whether
time to maturity, firm size, domestic financial development, and global financial distress
exacerbate or attenuate the effects of capital controls.
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5.1. Bond maturity
Capital account restrictions on inflows may have a stronger effect on the spread of bonds
maturing in the short-term for at least three reasons. First, existing evidence shows that the
introduction of capital controls shifts the composition of capital inflows to longer maturities,
making it difficult to roll over short-term debt (Forbes, 2007). Second, as documented by
Reinhart and Smith (2002), investors may expect capital controls to be transitory. In such a
case, investors would expect firms to face greater difficulty in rolling over debt maturing in
the short-term, but not necessarily debt maturing in the long-term. Third, we could also expect
a differentiated effect on the corporate bond spread as a function of time to maturity because
a longer time to maturity also grants firms more time to find alternative sources of financing.
To test this hypothesis, Column 1 of Table V adds the interaction of KA_IN and
KA_OUT with the bond time to maturity. The results show that bonds maturing in the shortterm are more affected by restrictions on capital inflows than bonds maturing in the long-run.
Specifically, a one-standard-deviation increase in KA_IN corresponds to a 114, 67, and 31
basis-point increase in the spreads of bonds with a time to maturity of 1 year, 5 years, and 8
years, respectively.
5.2. Firm size
Previous evidence supports the idea that firm size is a relevant variable for determining
the effects of capital controls on the cost of financing for firms. Both Forbes (2007) and
Edwards (1999) find that financial constraints were significantly greater for smaller firms than
for large firms during the encaje adopted in Chile between 1991 and 1998. Additionally, Alfaro,
15
Chari, and Kanczuk (2014) find that the cumulative abnormal stock returns of large firms were
less affected by the imposition of capital controls in Brazil during 2008-2009.
Column 2 of Table V shows that the effect of capital controls on credit spreads is
particularly strong for smaller firms. While an increase of one standard deviation in KA_IN
for the firms in the 75th percentile increases their corporate bond spreads by 19 basis points,
this effect grows to 69 basis points for firms in the 25th percentile.
The finding that capital controls have a heterogeneous effect depending on the size of the
firm implies that, in addition to increasing financing costs, capital controls also generate
inefficiencies in the allocation of capital across firms. Such inefficiencies in capital allocation
could have important negative effects on the economy since smaller firms are typically the
main drivers of growth and employment.
5.3. Domestic financial development
When a country imposes capital controls, a well-developed local financial system can act
as a substitute for firms’ financing needs. Additionally, more sophisticated local capital markets
potentially provide the scope for financial innovations that allow for circumvention of capital
controls (Klein and Olivei, 2008). Both effects reduce the negative impact of the capital
controls on the financing costs of firms in countries with well-developed domestic financial
markets.
In effect, Columns 3 and 4 of Table V show that higher levels of local financial
development, proxied by the ratios of private credit to GDP and private bond market
capitalization to GDP, reduce the negative impact of capital controls on corporate bond
spreads. The magnitude of the effect is both statistically and economically significant.
Specifically, a one-standard-deviation increase in KA_IN increases corporate bond spreads by
16
between 45 and 48 basis points in countries with a low level of local financial development,
corresponding to countries in the 25th percentile of private credit to GDP and private bond
market capitalization to GDP. This effect is much smaller (even negative) in countries with
high levels of local financial development: -16 and 4 basis points for countries in the 75th
percentile for the respective proxy variables.
5.4. Periods of financial distress
We have argued that, through a variety of channels, capital account restrictions on inflows
render accessing international sources of financing more difficult and/or expensive for firms.
Then, during periods of global financial distress, one would expect firms in countries with
capital account restrictions to face relatively deeper financing problems. Thus, when market
illiquidity and financial stability worsen, capital account restrictions should play a more
important role.
To test for this possibility, Column 5 adds the interactions of capital controls on inflows
(KA_IN) and capital controls on outflows (KA_OUT) with the Gamma measure of market
illiquidity. The Gamma measure, constructed by Bao, Pan, and Wang (2011) by using
information from the U.S. secondary corporate bond markets, corresponds to the negative of
the autocovariance of bond price changes. Since transitory price movements produce
negatively serially correlated price changes, the Gamma measure creates a meaningful measure
of debt market illiquidity that captures the impact of illiquidity on prices. Column 6 adds the
interactions of capital controls on inflows (KA_IN) and capital controls on outflows
(KA_OUT) with a measure of financial instability, namely, the VIX index. Specifically, the
VIX index is a measure of the implied volatility of the S&P500 index options.
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All the coefficients for the interaction terms between restrictions on inflows and the
measures of market illiquidity and financial instability are positive and highly significant. In
particular, a one-standard-deviation increase in KA_IN increases corporate bond spreads by
42 basis points when market liquidity is high (as indicated by values in the 25th percentile of
the Gamma measure) and by 52 basis points when market liquidity is low (as indicated by
values in the 75th percentile of the Gamma measure). Regarding the effect of financial stability,
a one-standard-deviation increase in KA_IN increases corporate bond spreads by 32 and 70
basis points when financial instability is low and high, respectively (as indicated by values in
the 25th and 75th percentiles of the VIX index, respectively). These results suggest that capital
account restrictions on inflows indeed have a greater effect during times of financial distress
than during times of financial stability.
6. Robustness
In this section, we perform a set of analyses to check whether our results could be driven
by biases arising from other factors. First, we explore whether the effect of capital account
restrictions on corporate bond spreads is affected by the specific type of transaction
(instrument) that is being restricted. We then examine whether our results are robust to the
use of different subsamples of countries and bonds. Overall, we show that our previous results
are robust to alternative specifications and subsamples.
6.1. Capital account restrictions by type of securities
Capital flows are far from being homogeneous, and each type of flow has its own
characteristics. The wide variety of characteristics might affect the way that bond spreads react
when capital controls are imposed in different types of capital flows. To rule out the possibility
18
that the heterogeneity of capital account restrictions could be biasing our results, we replicate
our baseline regressions while allowing for more disaggregated measures of capital account
restrictions. In particular, we take advantage of the disaggregation of the Schindler (2009) data
presented in Table I and explore the effect of imposing capital account restrictions on
corporate bond spreads for each type of transaction: shares, bonds, money market
instruments, and collective investments.
Table VI reports the results and shows that capital account restrictions on inflows continue
to have a positive and significant effect on corporate bond spreads for restrictions on the
issuance/trade of shares, bonds, and collective investments. Restrictions on money market
instruments are also positively correlated with credit spreads; however, the coefficient is not
statistically significant. The finding that the coefficient is considerably larger in the case of
restrictions on bonds than in the case of restrictions on the issuance/trade of other financial
assets is consistent with the status of debt as the primary financing tool for corporations. In
addition, the results show that restrictions on capital inflows tend to increase credit spreads
regardless of the type of transaction, suggesting that alternative sources of capital are
substitutes for each other. We do not find that capital controls on outflows have a robust,
significant effect on corporate bond spreads.
6.2. Additional robustness checks
In this subsection, we explore whether our results are robust to the use of different
subsamples. First, we replicate our baseline model while excluding high-income countries to
ensure that countries with higher degrees of development are not biasing the results. Such bias
may arise because liberalization is likely to be nonrandom, with policymakers more inclined to
open up when countries have reached a certain level of development. Column 1 of Table VII
19
shows that the coefficients for capital controls on inflows remain positive and highly
significant. In fact, this coefficient becomes stronger when we use the subsample of low- and
middle-income economies.
Next, we focus on ruling out potential biases that could arise from our measurement of
corporate bond spreads. The OAS analysis is a standard approach in financial markets for
computing the value of the eventual embedded option of bonds. However, this methodology
may introduce some errors into our dependent variable measurement. To explore whether the
use of OAS methodology is biasing our results, in Column 2 of Table VII, we replicate our
baseline specification while including only a sample of bonds without embedded options. Our
results remain qualitatively unchanged in comparison to our previous results.
7. Conclusions
Although a large body of research exists on the effects of capital account restrictions,
whether it is optimal for countries to liberalize their capital accounts remains an open empirical
question. This paper fills a gap in the literature by showing that capital account restrictions do
have a significant effect on the cost of debt capital for firms, as proxied by corporate bond
spreads, and that this effect is asymmetric across different types of restrictions.
The paper’s major finding is that capital account restrictions on inflows significantly
increase corporate bond spreads. Consistent with a causal interpretation, the results show that
this effect is particularly strong in bonds maturing in the short-term, issued by small firms, and
issued in countries with underdeveloped financial markets. Moreover, the paper shows that
capital account restrictions on inflows have a greater effect during periods of financial distress
than during periods of financial stability. Overall, these results are consistent with findings that
restrictions on capital inflows worsen the conditions for firms’ access to capital in international
20
markets, rendering them more vulnerable to shocks, particularly when financial markets are in
distress.
21
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25
Table I
Types of capital transactions potentially subject to restrictions
Inflows (KA_IN)
Outflows (KA_OUT)
Shares or other securities of a participating nature
Purchase locally by nonresidents
Sale or issue abroad by residents
Sale or issue locally by nonresidents
Purchase abroad by residents
Money market instruments
Purchase locally by nonresidents
Sale or issue abroad by residents
Sale or issue locally by nonresidents
Purchase abroad by residents
Bonds or other debt securities
Purchase locally by nonresidents
Sale or issue abroad by residents
Sale or issue locally by nonresidents
Purchase abroad by residents
Collective investment securities
Purchase locally by nonresidents
Sale or issue abroad by residents
Sale or issue locally by nonresidents
Purchase abroad by residents
26
Table II
Description of variables
This table describes the variables that are used in the empirical model, including the variable names, descriptions, units, and sources.
Description
Units
Source
Bond Characteristc
Option adjusted spread
Years to maturity
Issue size
Coupon rate
Option-adjusted spread
Years to maturity
Amount issued
Coupon bond
Basis points
Years
US$ (in log )
Basis points
Bloomberg
Bloomberg
Bloomberg
Bloomberg
Firm Specific
Equity volatility
Credit rating
Operating income to sales
Standard deviation of the day to day logarithmic price changes for the previous 180 days. Percent
S&P firm rating, long term debt, foreign currency
(1=D, …, 21=AAA)
Operating income divided by net sales.
Ratio
Bloomberg
S&P
Bloomberg
ST debt to total debt
Short term debt divided by total debt.
Ratio
Bloomberg
Total debt to asset
Total debt divided by total assets.
Ratio
Bloomberg
Size
Total assets
Millions of US$ (in log )
Bloomberg
Capital Account Restrictions
Capital account restrictions on inflows (KA_IN)
Capital account restrictions on outflows (KA_OUT)
Capital account restrictions on inflows: Shares
Capital account restrictions on outflows: Shares
Capital account restrictions on inflows: Bonds
Capital account restrictions on outflows: Bonds
Capital account restrictions on inflows: Money Market
Capital account restrictions on outflows: Money Market
Capital account restrictions on inflows: Collective Investment
Capital account restrictions on outflows: Collective Investment
Restrictions on capital inflows
Restrictions on capital outflows
Restrictions on share trading capital inflows
Restrictions on share trading capital outflows
Restrictions on bond trading capital inflows
Restrictions on bond trading capital outflows
Restrictions on money market capital inflows
Restrictions on money market capital outflows
Restrictions on collective investments capital inflows
Restrictions on collective investments capital outflows
Index: 0=unrestricted to 1=restricted
Index: 0=unrestricted to 1=restricted
Index: 0=unrestricted to 1=restricted
Index: 0=unrestricted to 1=restricted
Index: 0=unrestricted to 1=restricted
Index: 0=unrestricted to 1=restricted
Index: 0=unrestricted to 1=restricted
Index: 0=unrestricted to 1=restricted
Index: 0=unrestricted to 1=restricted
Index: 0=unrestricted to 1=restricted
Schindler (2009)
Schindler (2009)
Schindler (2009)
Schindler (2009)
Schindler (2009)
Schindler (2009)
Schindler (2009)
Schindler (2009)
Schindler (2009)
Schindler (2009)
Country Risk
Private credit to GDP
Private bond market capitalization to GDP
Public bond market capitalization to GDP
Trade to GDP
Political risk
Growth
GDP per capita
Sovereign credit rating
Private Credit divided by GDP
Private bond market capitalization divided by GDP
Public bond market capitalization divided by GDP
Exports plus imports divided by GDP
Assessment of the political stability in a country.
GDP growth rate
GDP divided by total population
S&P sovereign rating, long term debt, foreign currency
Ratio
Ratio
Ratio
Ratio
Index: 0=high risk to 100=low risk
Rate
Constant 2000 US$
(1=D, …, 21=AAA)
FDSD
FDSD
FDSD
WDI
ICRG
WDI
WDI
S&P
Distress Measures
Gamma measure
VIX
Negative of the autocovariance of price changes
Chicago Board Options Exchange Market Volatility Index
Basis points
Percentage points
Bao et al. (2010)
Bloomberg
27
Table III
Descriptive statistics
This table reports the descriptive statistics for all variables listed below.
Mean
Std. Dev.
Min.
Max.
Bond Characteristc
Option adjusted spread
Years to maturity
Issue size
Coupon rate
3.02
5.80
19.64
6.79
3.05
2.31
0.88
1.63
0.32
0.09
10.92
4.00
26.71
13.97
21.82
11.75
Firm Specific
Equity volatility
Credit rating
Operating income to sales
ST debt to total debt
Total debt to asset
Size
37.56
13.22
0.16
0.14
0.29
9.67
18.86
2.70
0.15
0.13
0.12
1.31
12.57
6
-0.87
0.00
0.00
5.38
140.69
20
0.72
0.94
0.77
12.74
Capital Account Restrictions
Restrictions on inflows (KA_IN)
Restrictions on outflows (KA_OUT)
Restrictions on inflows: Shares
Restrictions on outflows: Shares
Restrictions on inflows: Bonds
Restrictions on outflows: Bonds
Restrictions on inflows: Money Market
Restrictions on outflows: Money Market
Restrictions on inflows: Collective Investment
Restrictions on outflows: Collective Investment
0.17
0.24
0.36
0.25
0.09
0.24
0.09
0.21
0.12
0.25
0.24
0.36
0.32
0.39
0.25
0.37
0.25
0.36
0.31
0.40
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
Country Risk
Private credit to GDP
Private bond market capitalization to GDP
Public bond market capitalization to GDP
Trade to GDP
Political risk
Growth
GDP per capita
Sovereign credit rating
1.24
0.32
0.42
0.96
81.96
2.75
20.43
18.74
0.54
0.17
0.14
0.78
7.24
1.87
9.67
3.75
0.10
0.00
0.09
0.25
55.00
-0.26
1.03
1
1.84
0.73
0.92
4.38
94.00
10.67
41.21
21
Distress Measures
Gamma measure
VIX
18.99
22.29
24.79
10.98
3.09
10.27
103.19
60.72
28
Table IV
Corporate bond spreads and capital account restrictions
This table reports estimates from a panel regression of corporate option-adjusted spreads against the variables listed below.
All regressions control for bond and time fixed effects. The sample covers the period from 2005:Q1 to 2009:Q2. Robust
standard errors, clustered at the bond level, are presented in parentheses below each coefficient estimate. ***, **, and *
indicate significance at the 1%, 5%, and 10% levels, respectively.
Bond Characteristc
Years to maturity
Issue size
Coupon rate
Firm Specific
Equity volatility
Credit rating
Operating income to sales
ST debt to total debt
Total debt to asset
Size
Capital Account Restrictions
Capital account restrictions on inflows (KA_IN)
Capital account restrictions on outflows (KA_OUT)
Country Risk
Private credit to GDP
Private bond market capitalization to GDP
Public bond market capitalization to GDP
Trade to GDP
Political risk
Growth
GDP per capita
Sovereign credit rating
Observations
Adjusted R-squared
Industry Fixed Effects
Firm Fixed Effects
Time Fixed Effects
(1)
(2)
0.028
(0.028)
-0.020
(0.059)
0.132***
(0.042)
0.019
(0.013)
0.062
(0.052)
0.054
(0.036)
0.043***
(0.007)
-0.395***
(0.052)
-2.418***
(0.494)
1.564***
(0.565)
0.381
(0.561)
-0.048
(0.073)
0.037***
(0.008)
-0.682***
(0.145)
-0.792
(0.695)
2.459***
(0.683)
-0.752
(1.218)
0.215
(0.295)
1.546***
(0.528)
-1.022***
(0.242)
2.299***
(0.722)
-0.150
(0.292)
-0.586***
(0.204)
1.165**
(0.534)
0.858*
(0.451)
-0.023
(0.093)
0.018
(0.023)
0.027
(0.065)
0.012
(0.013)
-0.080
(0.075)
0.807
(0.674)
-2.885
(1.903)
8.882***
(1.969)
-2.605**
(1.062)
0.089***
(0.030)
-0.139***
(0.039)
0.137
(0.113)
-0.261*
(0.147)
3,740
0.694
YES
NO
YES
3,740
0.801
NO
YES
YES
29
Table V
Narrowing down the channels
This table reports estimates from a panel regression of corporate option-adjusted spreads against the variables listed below. The sample covers the period from 2005:Q1 to 2009:Q2. Robust
standard errors are presentedin parentheses; ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
KA_IN
KA_IN x Years to maturity
(1)
(2)
(3)
(4)
(5)
(6)
5.257***
(1.253)
-0.497***
(0.139)
14.041**
(5.635)
4.814***
(1.375)
4.225***
(1.325)
1.466***
(0.538)
0.006
(0.656)
KA_IN x Size
-1.268**
(0.596)
KA_IN x Private credit to GDP
-3.285**
(1.352)
KA_IN x Private bond market capitalization to GDP
-10.866**
(5.077)
KA_IN x Gamma
0.040***
(0.014)
KA_IN x VIX
KA_OUT
KA_OUT x Years to maturity
-1.542***
(0.489)
0.225***
(0.064)
KA_OUT x Size
0.132
(3.704)
0.961
(1.084)
0.022
(0.629)
-0.124
(0.384)
-0.023
(0.373)
KA_OUT x Private credit to GDP
-1.251
(1.128)
KA_OUT x Private bond market capitalization to GDP
0.108
(1.570)
KA_OUT x Gamma
-0.008
(0.009)
KA_OUT x VIX
Observations
Adjusted R-squared
Control Variables
Firm Fixed Effects
Time Fixed Effects
0.111***
(0.033)
0.320
(0.646)
-0.027
(0.020)
3,740
0.803
YES
YES
YES
3,740
0.802
YES
YES
YES
3,740
0.802
YES
YES
YES
3,740
0.802
YES
YES
YES
3,740
0.805
YES
YES
YES
3,740
0.805
YES
YES
YES
30
Table VI
Capital account restrictions by type of securities
This table reports estimates from a panel regression of corporate option-adjusted spreads against the variables listed below.
All regressions control for bond and time fixed effects. The sample covers the period from 2005:Q1 to 2009:Q2. Robust
standard errors, clustered at the bond level, are presented in parentheses below each coefficient estimate. ***, **, and *
indicate significance at the 1%, 5%, and 10% levels, respectively.
Bond Characteristc
Years to maturity
Issue size
Coupon rate
Firm Specific
Equity volatility
Credit rating
Operating income to sales
ST debt to total debt
Total debt to asset
Size
Capital Account Restrictions
Capital account restrictions on inflows
Capital account restrictions on outflows
Country Risk
Private credit to GDP
Private bond market capitalization to GDP
Public bond market capitalization to GDP
Trade to GDP
Political risk
Growth
GDP per capita
Sovereign credit rating
Observations
Adjusted R-squared
Firm Fixed Effects
Time Fixed Effects
Shares
Bonds
Money Market
(1)
(2)
(3)
Collective
Investment
(4)
0.019
(0.013)
0.062
(0.052)
0.055
(0.036)
0.018
(0.013)
0.060
(0.051)
0.054
(0.036)
0.019
(0.013)
0.062
(0.052)
0.057
(0.036)
0.019
(0.013)
0.063
(0.052)
0.057
(0.036)
0.037***
(0.008)
-0.679***
(0.145)
-0.836
(0.695)
2.444***
(0.682)
-0.641
(1.224)
0.218
(0.295)
0.037***
(0.008)
-0.688***
(0.145)
-0.708
(0.697)
2.469***
(0.681)
-0.761
(1.207)
0.227
(0.296)
0.036***
(0.008)
-0.661***
(0.147)
-0.879
(0.696)
2.485***
(0.680)
-0.737
(1.260)
0.180
(0.299)
0.036***
(0.008)
-0.670***
(0.145)
-0.902
(0.693)
2.491***
(0.681)
-0.880
(1.243)
0.191
(0.299)
0.556**
(0.248)
-0.349
(0.246)
3.417***
(0.843)
0.278
(0.247)
0.983
(0.666)
0.613
(0.377)
1.772***
(0.673)
-0.185
(0.259)
0.955
(0.697)
-3.130
(1.960)
8.652***
(1.992)
-2.359**
(1.122)
0.092***
(0.029)
-0.149***
(0.039)
0.147
(0.112)
-0.250*
(0.144)
0.077
(0.688)
-1.203
(1.964)
7.406***
(1.866)
-3.461***
(1.082)
0.082***
(0.029)
-0.125***
(0.039)
0.117
(0.112)
-0.308**
(0.145)
0.717
(0.687)
-2.569
(1.802)
9.444***
(2.262)
-3.077***
(1.060)
0.103***
(0.030)
-0.142***
(0.039)
0.145
(0.113)
-0.318**
(0.151)
1.120
(0.728)
-4.253**
(2.162)
10.437***
(2.436)
-2.994***
(1.039)
0.084***
(0.029)
-0.143***
(0.038)
0.144
(0.114)
-0.310*
(0.158)
3,740
0.801
YES
YES
3,740
0.802
YES
YES
3,740
0.801
YES
YES
3,740
0.801
YES
YES
31
Table VII
Subsamples
This table reports estimates from a panel regression of corporate option-adjusted spreads against the variables listed below.
All regressions control for bond and time fixed effects. The sample covers the period from 2005:Q1 to 2009:Q2. Robust
standard errors, clustered at the bond level, are presented in parentheses below each coefficient estimate. ***, **, and *
indicate significance at the 1%, 5%, and 10% levels, respectively.
Bond Characteristc
Years to maturity
Issue size
Coupon rate
Firm Specific
Equity volatility
Credit rating
Operating income to sales
ST debt to total debt
Total debt to asset
Size
Capital Account Restrictions
Capital account restrictions on inflows (KA_IN)
Capital account restrictions on outflows (KA_OUT)
Country Risk
Private credit to GDP
Private bond market capitalization to GDP
Public bond market capitalization to GDP
Trade to GDP
Political risk
Growth
GDP per capita
Sovereign credit rating
Observations
Adjusted R-squared
Firm Fixed Effects
Time Fixed Effects
Low and Middle Income
Countries
(1)
Bonds without Embedded
Options
(2)
0.036
(0.038)
0.434**
(0.190)
-0.013
(0.057)
0.045**
(0.018)
0.028
(0.069)
-0.002
(0.048)
0.065**
(0.029)
-0.957**
(0.370)
-1.003
(1.203)
0.729
(1.109)
1.137
(1.360)
0.479
(0.498)
-0.014
(0.010)
-0.331*
(0.186)
0.218
(0.971)
-0.001
(0.662)
1.613
(1.627)
0.913
(0.817)
3.400**
(1.449)
-0.164
(0.489)
3.244***
(0.901)
-0.450
(0.474)
13.937***
(3.587)
-24.823***
(8.223)
22.608***
(5.685)
-10.292***
(3.558)
0.078**
(0.034)
-0.313***
(0.086)
0.329
(0.668)
-0.551***
(0.178)
0.268
(1.404)
2.974
(3.864)
3.150*
(1.666)
-8.706***
(1.809)
0.036
(0.036)
-0.279***
(0.060)
0.698***
(0.191)
-0.363***
(0.137)
913
0.864
YES
YES
1,073
0.823
YES
YES
32
Centro de Economía Aplicada
Departamento de Ingeniería Industrial
Universidad de Chile
2015
307. Capital Controls and the Cost of Debt
Eugenia Andreasen, Martin Schindler y Patricio Valenzuela
2014
306. Assessing the extent of democratic failures. A 99%-Condorcet’s Jury Theorem.
Matteo Triossi
2013
305. The African Financial Development and Financial Inclusion Gaps
Franklin Allen, Elena Carletti, Robert Cull, Jun “Qj” Qian, Lemma Senbet y Patricio Valenzuela
304. Revealing Bargaining Power through Actual Wholesale Prices
Carlos Noton y Andrés Elberg
303. Structural Estimation of Price Adjustment Costs in the European Car Market
Carlos Noton
302. Remedies for Sick Insurance
Daniel McFadden, Carlos Noton y Pau Olivella
301. Minimum Coverage Regulation in Insurance Markets
Daniel McFadden, Carlos Noton y Pau Olivella
300. Rollover risk and corporate bond spreads
Patricio Valenzuela
299. Sovereign Ceilings “Lite”? The Impact of Sovereign Ratings on Corporate Ratings
Eduardo Borensztein, Kevin Cowan y Patricio Valenzuela
298. Improving Access to Banking: Evidence from Kenya
F. Allen, E. Carletti, R. Cull, J.“Qj” Qian, L. Senbet y P. Valenzuela
297. Financial Openness, Market Structure and Private Credit: An Empirical Investigation
Ronald Fischer y Patricio Valenzuela
296. Banking Competition and Economic Stability
Ronald Fischer, Nicolás Inostroza y Felipe J. Ramírez
295. Trust in Cohesive Communities
Felipe Balmaceda y Juan F. Escobar
294. A Spatial Model of Voting with Endogenous Proposals: Theory and Evidence from Chilean
Senate
Matteo Triossi, Patricio Valdivieso y Benjamín Villena-Roldán
2012
293. Participation in Organizations, Trust, and Social Capital Formation: Evidence from Chile
Patricio Valdivieso - Benjamín Villena-Roldán
292. Neutral Mergers Between Bilateral Markets
Antonio Romero-Medina y Matteo Triossi
291. On the Optimality of One-size-fits-all Contracts: The Limited Liability Case
Felipe Balmaceda
290. Self Governance in Social Networks of Information Transmission
Felipe Balmaceda y Juan F. Escobar
289. Efficiency in Games with Markovian Private Information
Juan F. Escobar y Juuso Toikka
288. EPL and Capital-Labor Ratios
Alexandre Janiak y Etienne Wasmer
287. Minimum Wages Strike Back: The Effects on Capital and Labor Demands in a Large-Firm
Framework
Sofía Bauducco y Alexandre Janiak
2011
286. Comments on Donahue and Zeckhauser: Collaborative Governance
Ronald Fischer
285. Casual Effects of Maternal Time-Investment on children’s Cognitive Outcomes
Benjamín Villena-Rodán y Cecilia Ríos-Aguilar
284. Towards a Quantitative Theory of Automatic Stabilizers: The Role of Demographics
Alexandre Janiak y Paulo Santos Monteiro
283. Investment and Environmental Regulation: Evidence on the Role of Cash Flow
Evangelina Dardati y Julio Riutort
282. Teachers’ Salaries in Latin America. How Much are They (under or over) Paid?
Alejandra Mizala y Hugo Ñopo
281. Acyclicity and Singleton Cores in Matching Markets
Antonio Romero-Medina y Matteo Triossi
280. Games with Capacity Manipulation: Incentives and Nash Equilibria
Antonio Romero-Medina y Matteo Triossi
279. Job Design and Incentives
Felipe Balmaceda
278. Unemployment, Participation and Worker Flows Over the Life Cycle
Sekyu Choi - Alexandre Janiak -Benjamín Villena-Roldán
277. Public-Private Partnerships and Infrastructure Provision in the United States
(Publicado como “Public-Private-Partnerships to Revamp U.S. Infrastructure”. Hamilton Policy
Brief, Brookings Institution 2011)
Eduardo Engel, Ronald Fischer y Alexander Galetovic
2010
276. The economics of infrastructure finance: Public-private partnerships versus public provision
(Publicado en European Investment Bank Papers, 15(1), pp 40-69.2010)
Eduardo Engel, Ronald Fischer y Alexander Galetovic
275. The Cost of Moral Hazard and Limited Liability in the Principal-Agent Problem
F. Balmaceda, S.R. Balseiro, J.R. Correa y N.E. Stier-Moses
274. Structural Unemployment and the Regulation of Product Market
Alexandre Janiak
273. Non-revelation Mechanisms in Many-to-One Markets
Antonio Romero-Medina y Matteo Triossi
272. Labor force heterogeneity: implications for the relation between aggregate volatility and
government size
Alexandre Janiak y Paulo Santos Monteiro
271. Aggregate Implications of Employer Search and Recruiting Selection
Benjamín Villena Roldán
270. Wage dispersion and Recruiting Selection
Benjamín Villena Roldán
269. Parental decisions in a choice based school system: Analyzing the transition between primary and
secondary school
Mattia Makovec, Alejandra Mizala y Andrés Barrera
268. Public-Private Wage Gap In Latin America (1999-2007): A Matching Approach
(Por aparecer en Labour Economics, (doi:10.1016/j.labeco.2011.08.004))
Alejandra Mizala, Pilar Romaguera y Sebastián Gallegos
267. Costly information acquisition. Better to toss a coin?
Matteo Triossi
266. Firm-Provided Training and Labor Market Institutions
Felipe Balmaceda
2009
265. Soft budgets and Renegotiations in Public-Private Partnerships
Eduardo Engel, Ronald Fischer y Alexander Galetovic
264. Information Asymmetries and an Endogenous Productivity Reversion Mechanism
Nicolás Figueroa y Oksana Leukhina
263. The Effectiveness of Private Voucher Education: Evidence from Structural School Switches
(Publicado en Educational Evaluation and Policy Analysis Vol. 33 Nº 2 2011. pp. 119-137)
Bernardo Lara, Alejandra Mizala y Andrea Repetto
262. Renegociación de concesiones en Chile
(Publicado como “Renegociación de Concesiones en Chile”. Estudios Públicos, 113, Verano,
151–205. 2009)
Eduardo Engel, Ronald Fischer, Alexander Galetovic y Manuel Hermosilla
261. Inflation and welfare in long-run equilibrium with firm dynamics
Alexandre Janiak y Paulo Santos Monteiro
260. Conflict Resolution in the Electricity Sector - The Experts Panel of Chile
R. Fischer, R. Palma-Behnke y J. Guevara-Cedeño
259. Economic Performance, creditor protection and labor inflexibility
(Publicado como “Economic Performance, creditor protection and labor inflexibility”. Oxford
Economic Papers, 62(3),553-577. 2010)
Felipe Balmaceda y Ronald Fischer
258. Effective Schools for Low Income Children: a Study of Chile’s Sociedad de Instrucción Primaria
(Publicado en Applied Economic Letters 19, 2012, pp. 445-451)
Francisco Henríquez, Alejandra Mizala y Andrea Repetto
257. Public-Private Partnerships: when and how
Eduardo Engel, Ronald Fischer y Alexander Galetovic
2008
256. Pricing with markups in industries with increasing marginal costs
José R. Correa, Nicolás Figueroa y Nicolás E. Stier-Moses
255. Implementation with renegotiation when preferences and feasible sets are state dependent
Luis Corchón y Matteo Triossi
254. Evaluación de estrategias de desarrollo para alcanzar los objetivos del Milenio en América Latina.
El caso de Chile
Raúl O’Ryan, Carlos J. de Miguel y Camilo Lagos
253. Welfare in models of trade with heterogeneous firms
Alexandre Janiak
252. Firm-Provided Training and Labor Market Policies
Felipe Balmaceda
251. Emerging Markets Variance Shocks: Local or International in Origin?
Viviana Fernández y Brian M. Lucey
* Para ver listado de números anteriores ir a http://www.cea-uchile.cl/.